{"id":"W4411047735","doi":"10.1016/j.gaitpost.2025.06.002","title":"Lumbar spine passive stiffness can be predicted using trunk moment of inertia","year":2025,"lang":"en","type":"article","venue":"Gait & Posture","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trunk; Stiffness; Lumbar; Moment of inertia; Moment (physics); Lumbar spine; Medicine; Inertia; Range of motion; Physical therapy; Anatomy; Physics; Structural engineering; Surgery; Engineering; Classical mechanics; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001930498,0.0006385284,0.0005290532,0.0008078838,0.0001614979,0.0005045098,0.0002106054,0.0006928582,0.001777917],"category_scores_gemma":[0.00164356,0.0003446954,0.0004184736,0.0003313658,0.0001488645,0.0005137983,0.0002467369,0.0002784209,0.0009655282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001479308,"about_ca_system_score_gemma":0.0001364369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002555819,"about_ca_topic_score_gemma":0.00498971,"domain_scores_codex":[0.999915,0.00001230059,0.00000683956,0.00002198428,0.00002833816,0.00001558467],"domain_scores_gemma":[0.9995965,0.0002165448,0.00008938517,0.00002045732,0.00004809485,0.00002897528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003162918,0.0006886744,0.6393892,0.0005131786,0.0004204694,0.001152371,0.0002580567,0.07200687,0.07407274,0.000361845,0.001993703,0.20598],"study_design_scores_gemma":[0.00007352333,0.0008240251,0.7963282,0.00008676857,0.0001512092,0.00110565,0.00007465202,0.1943297,0.005845785,0.0006348351,0.0004931899,0.00005240108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.948738,0.001032528,0.04480796,0.0001910816,0.00009913166,0.00005843232,0.00101505,0.0005388958,0.003518864],"genre_scores_gemma":[0.9965931,0.0002182804,0.002059081,0.00002184915,0.00003151929,0.00001744521,0.0002528899,0.00001718263,0.00078858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002555819,"threshold_uncertainty_score":0.005947709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009003998557984911,"score_gpt":0.2818615614671267,"score_spread":0.2728575629091418,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}